1,721,026 research outputs found
Comprehensive evaluation of lossless compression algorithms in a real use case for smart grid applications
No full textThe so-called “energy digitalization” is pervading the power and energy industry by providing many state-of-the-art digital technologies to collect, store, and analyze the very heterogeneous information managed in modern power systems. Requirements in terms of sampling frequency, temporal and user aggregation, measured quantities, measurement aggregations, involved players, and applications can be very different. Compression strategies, removing redundancy and over-specification in collected samples, can play a relevant role in efficiently move and store such heterogeneous data. In this work, a reference dataset from a real-world use case has been collected for comparing the performance of 62 lossless compression algorithms derived from the LZ77/LZSS/LZMA strategies. Compression of the six-day long acquisition, including active power measurements of a prosumer's system equipped with a Photovoltaic (PV) and a Battery Energy Storage System, highlights the different obtainable performance when the aggregation interval is changed from 15 min to 24 h. In particular, once the top performing algorithms have been selected, it has been possible to stress the impact of night–day cycle, mainly due to the different sparsity of PV-related data. The obtained results demonstrate that, globally, the compression ratio increases by increasing the aggregation interval, by reaching values close to 9.7. In particular, when offline operation is tolerated, optimal compression schemes can be easily applied, leading to consistent improvement of the compression ratio (up to 24%, depending on the actual algorithm and aggregation interval), which can be very significant when large number of data sources is considered
Analysis of Time Synchronization Challenges in Digital Twins for Edge-Enabled Data Centers in Smart Cities Scenario
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Design and Pilot Tests of a Transportable Off-Grid Charging Station for E-Bikes Powered by Renewables
No full textAssessment of Time Performance of Lightweight Virtualization for Edge Computing Applications
No full textAre Cloud Services Aware of Time? An Experimental Analysis oriented to Industry 4.0
No full textIn the last years, the industrial automation has experienced a deep transformation known as Industry4.0, and it is driven by Internet of Things (IoT) paradigm. The IoT-based automation is based on well-defined data models, which make easy the interaction among devices. Generally, the data generated by IoT sensors are elaborated to obtain value added services (such as predictive maintenance), exploiting cloud services and remote servers. An accurate timestamp of the data generated by sensors is required to maintain an adequate level of such services: an 'easy' task in the case of a new deployment, but a nightmare when existing plants or machinery are retrofitted. In this case, the data are timestamped at cloud level, using the remote time. In such situations, a knowledge of the sense of time of cloud services is fundamental to guarantee the quality of data elaboration. The target of the research is an experimental characterization and a comparison of time awareness of different commercial cloud service providers (i.e. Amazon AWS, Google Cloud and Microsoft Azure). The characterization highlights as, generally, the performance provided by different platform is comparable each other. The time offset of NTP (Network Time Protocol) clients running on different Virtual Machines (VMs) has an uncertainty ranging from 0.05 ms up to 0.6 ms depending by the client configuration. Such results demonstrate that extreme care must be taken when using the time of remote VMs. © 2019 IEEE.</p
Architecture for the interconnection of prototypical medical instrument via cloud services
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